The ideal observer is
a type of analysis that measures the
precision of a signal. To measure the precision of a neural
signal, a recording is made of the neuron's response to a pair
of repeating stimuli. Half of the responses are used to construct a probability histogram of the response amplitude, which defines the variability of the neural response to the stimuli. The other half of the responses are used to probe the histogram. The probability that a response was generated by one stimulus is compared with the probability that it was generated by the other stimulus. The one with the highest probability is chosen as the most likely stimulus, and the fraction of correct responses is calculated. When performed
for several pairs of stimuli varying e.g. in their contrast or
another parameter, this process generates a "neurometric function" that defines the neuron's ability to transmit information.
distribution contains several programs useful in
ideal observer analysis.